Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations88
Missing cells524
Missing cells (%)23.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.9 KiB
Average record size in memory208.0 B

Variable types

Categorical2
TimeSeries23

Timeseries statistics

Number of series23
Time series length88
Starting point1746193740000
Ending point1746198960000
Period60000
2025-05-02T17:16:33.358735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:33.608757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

pair has constant value "BTC/USD" Constant
candle_seconds has constant value "60" Constant
close is highly overall correlated with ema_14 and 16 other fieldsHigh correlation
ema_14 is highly overall correlated with close and 14 other fieldsHigh correlation
ema_21 is highly overall correlated with close and 16 other fieldsHigh correlation
ema_60 is highly overall correlated with close and 16 other fieldsHigh correlation
ema_7 is highly overall correlated with close and 17 other fieldsHigh correlation
high is highly overall correlated with close and 14 other fieldsHigh correlation
low is highly overall correlated with close and 17 other fieldsHigh correlation
macd_7 is highly overall correlated with close and 17 other fieldsHigh correlation
macdsignal_7 is highly overall correlated with ema_14 and 14 other fieldsHigh correlation
obv is highly overall correlated with close and 7 other fieldsHigh correlation
open is highly overall correlated with close and 16 other fieldsHigh correlation
rsi_14 is highly overall correlated with ema_21 and 7 other fieldsHigh correlation
rsi_21 is highly overall correlated with ema_60 and 3 other fieldsHigh correlation
rsi_60 is highly overall correlated with close and 13 other fieldsHigh correlation
rsi_7 is highly overall correlated with ema_21 and 5 other fieldsHigh correlation
sma_14 is highly overall correlated with close and 14 other fieldsHigh correlation
sma_21 is highly overall correlated with close and 16 other fieldsHigh correlation
sma_60 is highly overall correlated with close and 16 other fieldsHigh correlation
sma_7 is highly overall correlated with close and 17 other fieldsHigh correlation
volume is highly overall correlated with close and 7 other fieldsHigh correlation
window_end_ms is highly overall correlated with close and 15 other fieldsHigh correlation
window_start_ms is highly overall correlated with close and 15 other fieldsHigh correlation
sma_7 has 12 (13.6%) missing values Missing
sma_14 has 25 (28.4%) missing values Missing
sma_21 has 32 (36.4%) missing values Missing
sma_60 has 71 (80.7%) missing values Missing
ema_7 has 12 (13.6%) missing values Missing
ema_14 has 25 (28.4%) missing values Missing
ema_21 has 32 (36.4%) missing values Missing
ema_60 has 71 (80.7%) missing values Missing
rsi_7 has 14 (15.9%) missing values Missing
rsi_14 has 26 (29.5%) missing values Missing
rsi_21 has 33 (37.5%) missing values Missing
rsi_60 has 72 (81.8%) missing values Missing
macd_7 has 33 (37.5%) missing values Missing
macdsignal_7 has 33 (37.5%) missing values Missing
macdhist_7 has 33 (37.5%) missing values Missing
open is non stationary Non stationary
high is non stationary Non stationary
low is non stationary Non stationary
close is non stationary Non stationary
volume is non stationary Non stationary
window_start_ms is non stationary Non stationary
window_end_ms is non stationary Non stationary
sma_60 is non stationary Non stationary
ema_60 is non stationary Non stationary
rsi_14 is non stationary Non stationary
rsi_21 is non stationary Non stationary
rsi_60 is non stationary Non stationary
obv is non stationary Non stationary
window_start_ms is uniformly distributed Uniform
window_end_ms is uniformly distributed Uniform
volume has unique values Unique
window_start_ms has unique values Unique
window_end_ms has unique values Unique
obv has unique values Unique

Reproduction

Analysis started2025-05-02 15:16:08.311131
Analysis finished2025-05-02 15:16:33.266336
Duration24.96 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

pair
Categorical

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
BTC/USD
88 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters616
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBTC/USD
2nd rowBTC/USD
3rd rowBTC/USD
4th rowBTC/USD
5th rowBTC/USD

Common Values

ValueCountFrequency (%)
BTC/USD 88
100.0%

Length

2025-05-02T17:16:33.788615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T17:16:33.842604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
btc/usd 88
100.0%

Most occurring characters

ValueCountFrequency (%)
B 88
14.3%
T 88
14.3%
C 88
14.3%
/ 88
14.3%
U 88
14.3%
S 88
14.3%
D 88
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 88
14.3%
T 88
14.3%
C 88
14.3%
/ 88
14.3%
U 88
14.3%
S 88
14.3%
D 88
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 88
14.3%
T 88
14.3%
C 88
14.3%
/ 88
14.3%
U 88
14.3%
S 88
14.3%
D 88
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 88
14.3%
T 88
14.3%
C 88
14.3%
/ 88
14.3%
U 88
14.3%
S 88
14.3%
D 88
14.3%

open
Numeric time series

High correlation  Non stationary 

Distinct80
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94091.126
Minimum77084.8
Maximum97884.3
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:33.907711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum77084.8
5-th percentile81574.905
Q192680.25
median97213.65
Q397639.675
95-th percentile97854.22
Maximum97884.3
Range20799.5
Interquartile range (IQR)4959.425

Descriptive statistics

Standard deviation5904.5889
Coefficient of variation (CV)0.06275394
Kurtosis0.74147782
Mean94091.126
Median Absolute Deviation (MAD)452.2
Skewness-1.4762051
Sum8280019.1
Variance34864170
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.5335021111
2025-05-02T17:16:33.990510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:34.518430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:34.547614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
97208.3 4
 
4.5%
97624 2
 
2.3%
97829.4 2
 
2.3%
97836.8 2
 
2.3%
97804 2
 
2.3%
97254.7 2
 
2.3%
97604.7 1
 
1.1%
97599 1
 
1.1%
97561 1
 
1.1%
97469.1 1
 
1.1%
Other values (70) 70
79.5%
ValueCountFrequency (%)
77084.8 1
1.1%
78458.5 1
1.1%
78800 1
1.1%
80401.7 1
1.1%
80963.7 1
1.1%
82710 1
1.1%
82896.2 1
1.1%
82930 1
1.1%
83370 1
1.1%
84437.1 1
1.1%
ValueCountFrequency (%)
97884.3 1
1.1%
97876.7 1
1.1%
97875 1
1.1%
97867.5 1
1.1%
97863.6 1
1.1%
97836.8 2
2.3%
97829.4 2
2.3%
97804.1 1
1.1%
97804 2
2.3%
97758.5 1
1.1%
2025-05-02T17:16:34.132720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

high
Numeric time series

High correlation  Non stationary 

Distinct74
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94811.686
Minimum80398.9
Maximum97885
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:34.610310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum80398.9
5-th percentile84511.26
Q195358.775
median97272.95
Q397665.9
95-th percentile97872.96
Maximum97885
Range17486.1
Interquartile range (IQR)2307.125

Descriptive statistics

Standard deviation4884.6711
Coefficient of variation (CV)0.051519715
Kurtosis0.94666825
Mean94811.686
Median Absolute Deviation (MAD)428.25
Skewness-1.5532663
Sum8343428.4
Variance23860011
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3669722419
2025-05-02T17:16:34.701986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:34.920145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:34.953455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
97208.3 4
 
4.5%
97624 3
 
3.4%
97804.1 2
 
2.3%
97665.9 2
 
2.3%
97543.4 2
 
2.3%
97867.5 2
 
2.3%
97804 2
 
2.3%
97665.8 2
 
2.3%
97701.2 2
 
2.3%
96892 2
 
2.3%
Other values (64) 65
73.9%
ValueCountFrequency (%)
80398.9 1
1.1%
80800 1
1.1%
83548.9 1
1.1%
83971.5 1
1.1%
84482 1
1.1%
84565.6 1
1.1%
85278.7 1
1.1%
85372.3 1
1.1%
85611.7 1
1.1%
86401 1
1.1%
ValueCountFrequency (%)
97885 1
1.1%
97884.9 1
1.1%
97884.3 1
1.1%
97876.7 1
1.1%
97875.9 1
1.1%
97867.5 2
2.3%
97836.8 1
1.1%
97829.7 1
1.1%
97829.5 1
1.1%
97804.1 2
2.3%
2025-05-02T17:16:34.788954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

low
Numeric time series

High correlation  Non stationary 

Distinct81
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93268.169
Minimum74409.1
Maximum97876.7
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:35.017653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum74409.1
5-th percentile77886.885
Q191933.575
median97208.25
Q397617.725
95-th percentile97829.4
Maximum97876.7
Range23467.6
Interquartile range (IQR)5684.15

Descriptive statistics

Standard deviation7244.3221
Coefficient of variation (CV)0.077671966
Kurtosis0.33969883
Mean93268.169
Median Absolute Deviation (MAD)452.05
Skewness-1.3930003
Sum8207598.9
Variance52480203
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3736210635
2025-05-02T17:16:35.089514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:35.245805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:35.288157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
97208.2 3
 
3.4%
97254.7 2
 
2.3%
97617.7 2
 
2.3%
97829.4 2
 
2.3%
97604.6 2
 
2.3%
97836.7 2
 
2.3%
97364.3 1
 
1.1%
97543.4 1
 
1.1%
97543.3 1
 
1.1%
97535.3 1
 
1.1%
Other values (71) 71
80.7%
ValueCountFrequency (%)
74409.1 1
1.1%
74572.5 1
1.1%
75735.7 1
1.1%
77057.7 1
1.1%
77502.9 1
1.1%
78600 1
1.1%
78800 1
1.1%
79930.7 1
1.1%
81166 1
1.1%
81177.7 1
1.1%
ValueCountFrequency (%)
97876.7 1
1.1%
97875 1
1.1%
97836.7 2
2.3%
97829.4 2
2.3%
97804 1
1.1%
97803.9 1
1.1%
97793.2 1
1.1%
97787.9 1
1.1%
97758.5 1
1.1%
97737 1
1.1%
2025-05-02T17:16:35.136817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

close
Numeric time series

High correlation  Non stationary 

Distinct78
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94177.132
Minimum77084.8
Maximum97884.9
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:35.349364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum77084.8
5-th percentile81566.455
Q193693.225
median97236.85
Q397645.625
95-th percentile97856.755
Maximum97884.9
Range20800.1
Interquartile range (IQR)3952.4

Descriptive statistics

Standard deviation5901.391
Coefficient of variation (CV)0.062662675
Kurtosis0.84213454
Mean94177.132
Median Absolute Deviation (MAD)441.15
Skewness-1.5177236
Sum8287587.6
Variance34826416
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1493354771
2025-05-02T17:16:35.426633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:35.598112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:35.636624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
97208.3 4
 
4.5%
97829.4 2
 
2.3%
97254.7 2
 
2.3%
96892 2
 
2.3%
97624 2
 
2.3%
97665.8 2
 
2.3%
97543.4 2
 
2.3%
97804 2
 
2.3%
97451.6 1
 
1.1%
97604.6 1
 
1.1%
Other values (68) 68
77.3%
ValueCountFrequency (%)
77084.8 1
1.1%
78458.5 1
1.1%
78800 1
1.1%
80398.9 1
1.1%
80950.7 1
1.1%
82710 1
1.1%
82896.2 1
1.1%
82930 1
1.1%
83370 1
1.1%
84437 1
1.1%
ValueCountFrequency (%)
97884.9 1
1.1%
97884.3 1
1.1%
97876.7 1
1.1%
97875.9 1
1.1%
97867.5 1
1.1%
97836.8 1
1.1%
97836.7 1
1.1%
97829.4 2
2.3%
97804.1 1
1.1%
97804 2
2.3%
2025-05-02T17:16:35.470985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

volume
Numeric time series

High correlation  Non stationary  Unique 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1184.3325
Minimum0.00909259
Maximum6942.5572
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:35.702549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.00909259
5-th percentile0.066201653
Q11.1428574
median12.150111
Q32935.0286
95-th percentile5274.1399
Maximum6942.5572
Range6942.5481
Interquartile range (IQR)2933.8857

Descriptive statistics

Standard deviation1988.1625
Coefficient of variation (CV)1.6787198
Kurtosis0.28625524
Mean1184.3325
Median Absolute Deviation (MAD)12.118382
Skewness1.3438858
Sum104221.26
Variance3952790.2
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3953768888
2025-05-02T17:16:35.773245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:35.945256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:35.980042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.02358871 1
 
1.1%
0.06549571 1
 
1.1%
35.72536297 1
 
1.1%
0.44410517 1
 
1.1%
0.5452669 1
 
1.1%
1.4390618 1
 
1.1%
1.37639748 1
 
1.1%
0.47967044 1
 
1.1%
0.0398695 1
 
1.1%
0.99766936 1
 
1.1%
Other values (78) 78
88.6%
ValueCountFrequency (%)
0.00909259 1
1.1%
0.01447002 1
1.1%
0.02358871 1
1.1%
0.0398695 1
1.1%
0.06549571 1
1.1%
0.06751269 1
1.1%
0.07131972 1
1.1%
0.1566265 1
1.1%
0.15855975 1
1.1%
0.1670844 1
1.1%
ValueCountFrequency (%)
6942.557214 1
1.1%
6169.892114 1
1.1%
5813.447481 1
1.1%
5650.73679 1
1.1%
5503.569224 1
1.1%
4848.056979 1
1.1%
4834.93225 1
1.1%
4690.111921 1
1.1%
4649.273713 1
1.1%
4223.466789 1
1.1%
2025-05-02T17:16:35.825506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

window_start_ms
Numeric time series

High correlation  Non stationary  Uniform  Unique 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7461964 × 1012
Minimum1.7461937 × 1012
Maximum1.746199 × 1012
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:36.045238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.7461937 × 1012
5-th percentile1.746194 × 1012
Q11.746195 × 1012
median1.7461964 × 1012
Q31.7461977 × 1012
95-th percentile1.7461987 × 1012
Maximum1.746199 × 1012
Range5220000
Interquartile range (IQR)2610000

Descriptive statistics

Standard deviation1532840.5
Coefficient of variation (CV)8.778168 × 10-7
Kurtosis-1.2
Mean1.7461964 × 1012
Median Absolute Deviation (MAD)1320000
Skewness0
Sum1.5366528 × 1014
Variance2.3496 × 1012
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value0.9585325863
2025-05-02T17:16:36.131227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:36.312532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:36.343107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.74619374 × 10121
 
1.1%
1.7461938 × 10121
 
1.1%
1.74619758 × 10121
 
1.1%
1.74619752 × 10121
 
1.1%
1.74619746 × 10121
 
1.1%
1.7461974 × 10121
 
1.1%
1.74619734 × 10121
 
1.1%
1.74619728 × 10121
 
1.1%
1.74619722 × 10121
 
1.1%
1.74619716 × 10121
 
1.1%
Other values (78) 78
88.6%
ValueCountFrequency (%)
1.74619374 × 10121
1.1%
1.7461938 × 10121
1.1%
1.74619386 × 10121
1.1%
1.74619392 × 10121
1.1%
1.74619398 × 10121
1.1%
1.74619404 × 10121
1.1%
1.7461941 × 10121
1.1%
1.74619416 × 10121
1.1%
1.74619422 × 10121
1.1%
1.74619428 × 10121
1.1%
ValueCountFrequency (%)
1.74619896 × 10121
1.1%
1.7461989 × 10121
1.1%
1.74619884 × 10121
1.1%
1.74619878 × 10121
1.1%
1.74619872 × 10121
1.1%
1.74619866 × 10121
1.1%
1.7461986 × 10121
1.1%
1.74619854 × 10121
1.1%
1.74619848 × 10121
1.1%
1.74619842 × 10121
1.1%
2025-05-02T17:16:36.181773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

window_end_ms
Numeric time series

High correlation  Non stationary  Uniform  Unique 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7461964 × 1012
Minimum1.7461938 × 1012
Maximum1.746199 × 1012
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:36.406157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.7461938 × 1012
5-th percentile1.7461941 × 1012
Q11.7461951 × 1012
median1.7461964 × 1012
Q31.7461977 × 1012
95-th percentile1.7461988 × 1012
Maximum1.746199 × 1012
Range5220000
Interquartile range (IQR)2610000

Descriptive statistics

Standard deviation1532840.5
Coefficient of variation (CV)8.7781677 × 10-7
Kurtosis-1.2
Mean1.7461964 × 1012
Median Absolute Deviation (MAD)1320000
Skewness0
Sum1.5366528 × 1014
Variance2.3496 × 1012
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value0.9585313531
2025-05-02T17:16:36.473129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:36.651299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:58.709207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.7461938 × 10121
 
1.1%
1.74619386 × 10121
 
1.1%
1.74619764 × 10121
 
1.1%
1.74619758 × 10121
 
1.1%
1.74619752 × 10121
 
1.1%
1.74619746 × 10121
 
1.1%
1.7461974 × 10121
 
1.1%
1.74619734 × 10121
 
1.1%
1.74619728 × 10121
 
1.1%
1.74619722 × 10121
 
1.1%
Other values (78) 78
88.6%
ValueCountFrequency (%)
1.7461938 × 10121
1.1%
1.74619386 × 10121
1.1%
1.74619392 × 10121
1.1%
1.74619398 × 10121
1.1%
1.74619404 × 10121
1.1%
1.7461941 × 10121
1.1%
1.74619416 × 10121
1.1%
1.74619422 × 10121
1.1%
1.74619428 × 10121
1.1%
1.74619434 × 10121
1.1%
ValueCountFrequency (%)
1.74619902 × 10121
1.1%
1.74619896 × 10121
1.1%
1.7461989 × 10121
1.1%
1.74619884 × 10121
1.1%
1.74619878 × 10121
1.1%
1.74619872 × 10121
1.1%
1.74619866 × 10121
1.1%
1.7461986 × 10121
1.1%
1.74619854 × 10121
1.1%
1.74619848 × 10121
1.1%
2025-05-02T17:16:36.527742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

candle_seconds
Categorical

Constant 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
60
88 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60
2nd row60
3rd row60
4th row60
5th row60

Common Values

ValueCountFrequency (%)
60 88
100.0%

Length

2025-05-02T17:16:36.249310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-02T17:16:36.278537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
60 88
100.0%

Most occurring characters

ValueCountFrequency (%)
6 88
50.0%
0 88
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 88
50.0%
0 88
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 88
50.0%
0 88
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 88
50.0%
0 88
50.0%

sma_7
Numeric time series

High correlation  Missing 

Distinct76
Distinct (%)100.0%
Missing12
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean94107.319
Minimum81335.914
Maximum97866.114
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:36.316831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum81335.914
5-th percentile82092.554
Q192330.586
median97444.364
Q397675.321
95-th percentile97846.989
Maximum97866.114
Range16530.2
Interquartile range (IQR)5344.7357

Descriptive statistics

Standard deviation5778.9018
Coefficient of variation (CV)0.06140757
Kurtosis-0.16404486
Mean94107.319
Median Absolute Deviation (MAD)374.92857
Skewness-1.2743369
Sum7152156.2
Variance33395706
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.38526819 × 10-13
2025-05-02T17:16:36.394132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:36.671913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps1
min420000
max420000
mean420000
std0
2025-05-02T17:16:36.707359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
97500.65714 1
 
1.1%
97714 1
 
1.1%
97681.21429 1
 
1.1%
97640.35714 1
 
1.1%
97612.68571 1
 
1.1%
97602.78571 1
 
1.1%
97572.17143 1
 
1.1%
97540.01429 1
 
1.1%
97475.11429 1
 
1.1%
97474.61429 1
 
1.1%
Other values (66) 66
75.0%
(Missing) 12
 
13.6%
ValueCountFrequency (%)
81335.91429 1
1.1%
81354.42857 1
1.1%
81565.84286 1
1.1%
81677.25714 1
1.1%
82230.98571 1
1.1%
83124.2 1
1.1%
83610.84286 1
1.1%
84109.78571 1
1.1%
84172.64286 1
1.1%
84393.12857 1
1.1%
ValueCountFrequency (%)
97866.11429 1
1.1%
97858.18571 1
1.1%
97850.54286 1
1.1%
97850.34286 1
1.1%
97845.87143 1
1.1%
97841.18571 1
1.1%
97830.92857 1
1.1%
97830.4 1
1.1%
97808.18571 1
1.1%
97791.64286 1
1.1%
2025-05-02T17:16:36.492455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

sma_14
Numeric time series

High correlation  Missing 

Distinct63
Distinct (%)100.0%
Missing25
Missing (%)28.4%
Infinite0
Infinite (%)0.0%
Mean94164.109
Minimum82732.107
Maximum97830.664
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:36.792582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum82732.107
5-th percentile83426.704
Q191346.764
median97451.957
Q397720.575
95-th percentile97815.983
Maximum97830.664
Range15098.557
Interquartile range (IQR)6373.8107

Descriptive statistics

Standard deviation5459.6451
Coefficient of variation (CV)0.057980106
Kurtosis-0.33484972
Mean94164.109
Median Absolute Deviation (MAD)344.72143
Skewness-1.1934301
Sum5932338.9
Variance29807724
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.361117923 × 10-6
2025-05-02T17:16:36.886769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:37.387677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:37.422148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
84849.60714 1
 
1.1%
97495.93571 1
 
1.1%
97516.55714 1
 
1.1%
97529.10714 1
 
1.1%
97533.87857 1
 
1.1%
97538.24286 1
 
1.1%
97541.23571 1
 
1.1%
97544.35 1
 
1.1%
97552.42857 1
 
1.1%
97578.16429 1
 
1.1%
Other values (53) 53
60.2%
(Missing) 25
28.4%
ValueCountFrequency (%)
82732.10714 1
1.1%
82869.24286 1
1.1%
83304.85714 1
1.1%
83422.75714 1
1.1%
83462.22857 1
1.1%
83758.66429 1
1.1%
84106.67143 1
1.1%
84289.52143 1
1.1%
84795.10714 1
1.1%
84849.60714 1
1.1%
ValueCountFrequency (%)
97830.66429 1
1.1%
97829.26429 1
1.1%
97821.26429 1
1.1%
97816.41429 1
1.1%
97812.1 1
1.1%
97806.55 1
1.1%
97800.22143 1
1.1%
97796.67857 1
1.1%
97790.05714 1
1.1%
97786.08571 1
1.1%
2025-05-02T17:16:36.963594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

sma_21
Numeric time series

High correlation  Missing 

Distinct56
Distinct (%)100.0%
Missing32
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean94627.355
Minimum84274.471
Maximum97781.086
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:37.482280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum84274.471
5-th percentile84863.65
Q191809.946
median97427.543
Q397677.026
95-th percentile97778.521
Maximum97781.086
Range13506.614
Interquartile range (IQR)5867.0798

Descriptive statistics

Standard deviation4538.0468
Coefficient of variation (CV)0.047957029
Kurtosis-0.071388494
Mean94627.355
Median Absolute Deviation (MAD)348.5
Skewness-1.2162756
Sum5299131.9
Variance20593869
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.869745205 × 10-9
2025-05-02T17:16:37.545842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:37.731614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:37.767778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
97436.86667 1
 
1.1%
97480.44286 1
 
1.1%
97502.23333 1
 
1.1%
97521.10476 1
 
1.1%
97544.07619 1
 
1.1%
97571.44286 1
 
1.1%
97590.7381 1
 
1.1%
97603.52381 1
 
1.1%
97614.57143 1
 
1.1%
97624.70476 1
 
1.1%
Other values (46) 46
52.3%
(Missing) 32
36.4%
ValueCountFrequency (%)
84274.47143 1
1.1%
84393.84762 1
1.1%
84734.91429 1
1.1%
84906.5619 1
1.1%
85583.00952 1
1.1%
86460.10476 1
1.1%
87141.15238 1
1.1%
87749.30952 1
1.1%
88288.99524 1
1.1%
88787.3 1
1.1%
ValueCountFrequency (%)
97781.08571 1
1.1%
97779.8 1
1.1%
97779.4 1
1.1%
97778.22857 1
1.1%
97777.50476 1
1.1%
97774.58095 1
1.1%
97773.55238 1
1.1%
97766.29524 1
1.1%
97765.8 1
1.1%
97758.00476 1
1.1%
2025-05-02T17:16:37.599554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

sma_60
Numeric time series

High correlation  Missing  Non stationary 

Distinct17
Distinct (%)100.0%
Missing71
Missing (%)80.7%
Infinite0
Infinite (%)0.0%
Mean94342.37
Minimum92691.12
Maximum96332.075
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:37.840932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum92691.12
5-th percentile92785.476
Q193364.37
median94349.728
Q395133.895
95-th percentile96057.651
Maximum96332.075
Range3640.955
Interquartile range (IQR)1769.525

Descriptive statistics

Standard deviation1138.9657
Coefficient of variation (CV)0.012072685
Kurtosis-1.0707341
Mean94342.37
Median Absolute Deviation (MAD)985.35833
Skewness0.14698091
Sum1603820.3
Variance1297242.9
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value0.9984599635
2025-05-02T17:16:37.898220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
2025-05-02T17:16:38.051664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:38.082485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
92691.12 1
 
1.1%
95989.045 1
 
1.1%
95701.71167 1
 
1.1%
95382.35667 1
 
1.1%
95133.895 1
 
1.1%
94948.38333 1
 
1.1%
94709.53 1
 
1.1%
94532.62167 1
 
1.1%
94349.72833 1
 
1.1%
94152.35167 1
 
1.1%
Other values (7) 7
 
8.0%
(Missing) 71
80.7%
ValueCountFrequency (%)
92691.12 1
1.1%
92809.065 1
1.1%
92996.14333 1
1.1%
93117.22833 1
1.1%
93364.37 1
1.1%
93681.10333 1
1.1%
93929.56667 1
1.1%
94152.35167 1
1.1%
94349.72833 1
1.1%
94532.62167 1
1.1%
ValueCountFrequency (%)
96332.075 1
1.1%
95989.045 1
1.1%
95701.71167 1
1.1%
95382.35667 1
1.1%
95133.895 1
1.1%
94948.38333 1
1.1%
94709.53 1
1.1%
94532.62167 1
1.1%
94349.72833 1
1.1%
94152.35167 1
1.1%
2025-05-02T17:16:37.938650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

ema_7
Numeric time series

High correlation  Missing 

Distinct76
Distinct (%)100.0%
Missing12
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean94107.319
Minimum81335.914
Maximum97866.114
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:38.136260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum81335.914
5-th percentile82092.554
Q192330.586
median97444.364
Q397675.321
95-th percentile97846.989
Maximum97866.114
Range16530.2
Interquartile range (IQR)5344.7357

Descriptive statistics

Standard deviation5778.9018
Coefficient of variation (CV)0.06140757
Kurtosis-0.16404486
Mean94107.319
Median Absolute Deviation (MAD)374.92857
Skewness-1.2743369
Sum7152156.2
Variance33395706
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.38526819 × 10-13
2025-05-02T17:16:38.198124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:38.386199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps1
min420000
max420000
mean420000
std0
2025-05-02T17:16:38.435076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
97500.65714 1
 
1.1%
97714 1
 
1.1%
97681.21429 1
 
1.1%
97640.35714 1
 
1.1%
97612.68571 1
 
1.1%
97602.78571 1
 
1.1%
97572.17143 1
 
1.1%
97540.01429 1
 
1.1%
97475.11429 1
 
1.1%
97474.61429 1
 
1.1%
Other values (66) 66
75.0%
(Missing) 12
 
13.6%
ValueCountFrequency (%)
81335.91429 1
1.1%
81354.42857 1
1.1%
81565.84286 1
1.1%
81677.25714 1
1.1%
82230.98571 1
1.1%
83124.2 1
1.1%
83610.84286 1
1.1%
84109.78571 1
1.1%
84172.64286 1
1.1%
84393.12857 1
1.1%
ValueCountFrequency (%)
97866.11429 1
1.1%
97858.18571 1
1.1%
97850.54286 1
1.1%
97850.34286 1
1.1%
97845.87143 1
1.1%
97841.18571 1
1.1%
97830.92857 1
1.1%
97830.4 1
1.1%
97808.18571 1
1.1%
97791.64286 1
1.1%
2025-05-02T17:16:38.244547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

ema_14
Numeric time series

High correlation  Missing 

Distinct63
Distinct (%)100.0%
Missing25
Missing (%)28.4%
Infinite0
Infinite (%)0.0%
Mean94164.109
Minimum82732.107
Maximum97830.664
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:38.532963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum82732.107
5-th percentile83426.704
Q191346.764
median97451.957
Q397720.575
95-th percentile97815.983
Maximum97830.664
Range15098.557
Interquartile range (IQR)6373.8107

Descriptive statistics

Standard deviation5459.6451
Coefficient of variation (CV)0.057980106
Kurtosis-0.33484972
Mean94164.109
Median Absolute Deviation (MAD)344.72143
Skewness-1.1934301
Sum5932338.9
Variance29807724
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.361117923 × 10-6
2025-05-02T17:16:38.622737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:38.836235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:38.874905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
84849.60714 1
 
1.1%
97495.93571 1
 
1.1%
97516.55714 1
 
1.1%
97529.10714 1
 
1.1%
97533.87857 1
 
1.1%
97538.24286 1
 
1.1%
97541.23571 1
 
1.1%
97544.35 1
 
1.1%
97552.42857 1
 
1.1%
97578.16429 1
 
1.1%
Other values (53) 53
60.2%
(Missing) 25
28.4%
ValueCountFrequency (%)
82732.10714 1
1.1%
82869.24286 1
1.1%
83304.85714 1
1.1%
83422.75714 1
1.1%
83462.22857 1
1.1%
83758.66429 1
1.1%
84106.67143 1
1.1%
84289.52143 1
1.1%
84795.10714 1
1.1%
84849.60714 1
1.1%
ValueCountFrequency (%)
97830.66429 1
1.1%
97829.26429 1
1.1%
97821.26429 1
1.1%
97816.41429 1
1.1%
97812.1 1
1.1%
97806.55 1
1.1%
97800.22143 1
1.1%
97796.67857 1
1.1%
97790.05714 1
1.1%
97786.08571 1
1.1%
2025-05-02T17:16:38.682104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

ema_21
Numeric time series

High correlation  Missing 

Distinct56
Distinct (%)100.0%
Missing32
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean94627.355
Minimum84274.471
Maximum97781.086
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:38.940850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum84274.471
5-th percentile84863.65
Q191809.946
median97427.543
Q397677.026
95-th percentile97778.521
Maximum97781.086
Range13506.614
Interquartile range (IQR)5867.0798

Descriptive statistics

Standard deviation4538.0468
Coefficient of variation (CV)0.047957029
Kurtosis-0.071388494
Mean94627.355
Median Absolute Deviation (MAD)348.5
Skewness-1.2162756
Sum5299131.9
Variance20593869
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.869745205 × 10-9
2025-05-02T17:16:39.010119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:39.223577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:39.260247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
97436.86667 1
 
1.1%
97480.44286 1
 
1.1%
97502.23333 1
 
1.1%
97521.10476 1
 
1.1%
97544.07619 1
 
1.1%
97571.44286 1
 
1.1%
97590.7381 1
 
1.1%
97603.52381 1
 
1.1%
97614.57143 1
 
1.1%
97624.70476 1
 
1.1%
Other values (46) 46
52.3%
(Missing) 32
36.4%
ValueCountFrequency (%)
84274.47143 1
1.1%
84393.84762 1
1.1%
84734.91429 1
1.1%
84906.5619 1
1.1%
85583.00952 1
1.1%
86460.10476 1
1.1%
87141.15238 1
1.1%
87749.30952 1
1.1%
88288.99524 1
1.1%
88787.3 1
1.1%
ValueCountFrequency (%)
97781.08571 1
1.1%
97779.8 1
1.1%
97779.4 1
1.1%
97778.22857 1
1.1%
97777.50476 1
1.1%
97774.58095 1
1.1%
97773.55238 1
1.1%
97766.29524 1
1.1%
97765.8 1
1.1%
97758.00476 1
1.1%
2025-05-02T17:16:39.066247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

ema_60
Numeric time series

High correlation  Missing  Non stationary 

Distinct17
Distinct (%)100.0%
Missing71
Missing (%)80.7%
Infinite0
Infinite (%)0.0%
Mean94342.37
Minimum92691.12
Maximum96332.075
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:39.339607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum92691.12
5-th percentile92785.476
Q193364.37
median94349.728
Q395133.895
95-th percentile96057.651
Maximum96332.075
Range3640.955
Interquartile range (IQR)1769.525

Descriptive statistics

Standard deviation1138.9657
Coefficient of variation (CV)0.012072685
Kurtosis-1.0707341
Mean94342.37
Median Absolute Deviation (MAD)985.35833
Skewness0.14698091
Sum1603820.3
Variance1297242.9
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value0.9984599635
2025-05-02T17:16:39.395993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
2025-05-02T17:16:39.568284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:39.601987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
92691.12 1
 
1.1%
95989.045 1
 
1.1%
95701.71167 1
 
1.1%
95382.35667 1
 
1.1%
95133.895 1
 
1.1%
94948.38333 1
 
1.1%
94709.53 1
 
1.1%
94532.62167 1
 
1.1%
94349.72833 1
 
1.1%
94152.35167 1
 
1.1%
Other values (7) 7
 
8.0%
(Missing) 71
80.7%
ValueCountFrequency (%)
92691.12 1
1.1%
92809.065 1
1.1%
92996.14333 1
1.1%
93117.22833 1
1.1%
93364.37 1
1.1%
93681.10333 1
1.1%
93929.56667 1
1.1%
94152.35167 1
1.1%
94349.72833 1
1.1%
94532.62167 1
1.1%
ValueCountFrequency (%)
96332.075 1
1.1%
95989.045 1
1.1%
95701.71167 1
1.1%
95382.35667 1
1.1%
95133.895 1
1.1%
94948.38333 1
1.1%
94709.53 1
1.1%
94532.62167 1
1.1%
94349.72833 1
1.1%
94152.35167 1
1.1%
2025-05-02T17:16:39.440139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

rsi_7
Numeric time series

High correlation  Missing 

Distinct66
Distinct (%)89.2%
Missing14
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean61.830053
Minimum0.10712373
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:39.665701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.10712373
5-th percentile17.226904
Q133.437366
median67.271757
Q387.257332
95-th percentile100
Maximum100
Range99.892876
Interquartile range (IQR)53.819966

Descriptive statistics

Standard deviation30.689263
Coefficient of variation (CV)0.49634864
Kurtosis-1.2249476
Mean61.830053
Median Absolute Deviation (MAD)31.039617
Skewness-0.23230866
Sum4575.4239
Variance941.83085
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.02840536372
2025-05-02T17:16:39.738803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:40.131081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps1
min480000
max480000
mean480000
std0
2025-05-02T17:16:40.174579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
100 9
 
10.2%
68.05627931 1
 
1.1%
74.06364404 1
 
1.1%
81.097749 1
 
1.1%
82.59017325 1
 
1.1%
84.99755262 1
 
1.1%
80.61966488 1
 
1.1%
87.18801997 1
 
1.1%
66.4872347 1
 
1.1%
66.01983614 1
 
1.1%
Other values (56) 56
63.6%
(Missing) 14
 
15.9%
ValueCountFrequency (%)
0.1071237279 1
1.1%
0.1207729469 1
1.1%
1.165871754 1
1.1%
16.58181818 1
1.1%
17.57425743 1
1.1%
17.81720015 1
1.1%
20.16491754 1
1.1%
22.87496751 1
1.1%
23.46640701 1
1.1%
26.44330199 1
1.1%
ValueCountFrequency (%)
100 9
10.2%
99.97441801 1
 
1.1%
99.95372513 1
 
1.1%
99.82867121 1
 
1.1%
99.82363316 1
 
1.1%
99.75989288 1
 
1.1%
99.47248288 1
 
1.1%
99.39031505 1
 
1.1%
99.30925075 1
 
1.1%
90.74797236 1
 
1.1%
2025-05-02T17:16:39.929291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

rsi_14
Numeric time series

High correlation  Missing  Non stationary 

Distinct61
Distinct (%)98.4%
Missing26
Missing (%)29.5%
Infinite0
Infinite (%)0.0%
Mean65.730114
Minimum15.363808
Maximum99.896356
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:40.248103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.363808
5-th percentile23.378502
Q153.747414
median64.412488
Q384.050892
95-th percentile99.831284
Maximum99.896356
Range84.532548
Interquartile range (IQR)30.303479

Descriptive statistics

Standard deviation22.993626
Coefficient of variation (CV)0.34981874
Kurtosis-0.54453371
Mean65.730114
Median Absolute Deviation (MAD)17.148458
Skewness-0.29680313
Sum4075.2671
Variance528.70682
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.4801580539
2025-05-02T17:16:40.342147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:40.780172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:40.815979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
66.36435778 2
 
2.3%
82.20289855 1
 
1.1%
61.42244182 1
 
1.1%
55.05907301 1
 
1.1%
54.66768526 1
 
1.1%
53.29765465 1
 
1.1%
53.44065657 1
 
1.1%
58.04295264 1
 
1.1%
78.1264637 1
 
1.1%
84.4556962 1
 
1.1%
Other values (51) 51
58.0%
(Missing) 26
29.5%
ValueCountFrequency (%)
15.36380849 1
1.1%
19.3556701 1
1.1%
23.0636833 1
1.1%
23.32761578 1
1.1%
24.34534612 1
1.1%
26.51793279 1
1.1%
33.63937771 1
1.1%
35.09845663 1
1.1%
38.45648444 1
1.1%
39.0450272 1
1.1%
ValueCountFrequency (%)
99.8963561 1
1.1%
99.87177442 1
1.1%
99.83145433 1
1.1%
99.83142306 1
1.1%
99.82864779 1
1.1%
99.76461048 1
1.1%
99.51775537 1
1.1%
99.45352334 1
1.1%
99.39268902 1
1.1%
89.77992664 1
1.1%
2025-05-02T17:16:40.653836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

rsi_21
Numeric time series

High correlation  Missing  Non stationary 

Distinct55
Distinct (%)100.0%
Missing33
Missing (%)37.5%
Infinite0
Infinite (%)0.0%
Mean70.036824
Minimum34.483416
Maximum99.898355
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:40.883599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum34.483416
5-th percentile47.775284
Q162.020067
median68.385173
Q373.667664
95-th percentile99.348764
Maximum99.898355
Range65.414939
Interquartile range (IQR)11.647597

Descriptive statistics

Standard deviation15.376549
Coefficient of variation (CV)0.21954949
Kurtosis-0.047771402
Mean70.036824
Median Absolute Deviation (MAD)6.0862704
Skewness0.35544119
Sum3852.0253
Variance236.43826
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.8422963449
2025-05-02T17:16:40.948947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:41.154160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:41.205136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
51.94068858 1
 
1.1%
71.7697431 1
 
1.1%
71.7697431 1
 
1.1%
67.81443855 1
 
1.1%
70.12683578 1
 
1.1%
72.26311304 1
 
1.1%
68.06992508 1
 
1.1%
63.63636364 1
 
1.1%
61.48969889 1
 
1.1%
60.74313409 1
 
1.1%
Other values (45) 45
51.1%
(Missing) 33
37.5%
ValueCountFrequency (%)
34.48341594 1
1.1%
41.66666667 1
1.1%
46.83644001 1
1.1%
48.17764579 1
1.1%
51.08169656 1
1.1%
51.94068858 1
1.1%
52.60908019 1
1.1%
53.13963292 1
1.1%
55.87442137 1
1.1%
56.79924751 1
1.1%
ValueCountFrequency (%)
99.89835471 1
1.1%
99.87496336 1
1.1%
99.34926938 1
1.1%
99.34854685 1
1.1%
98.14124369 1
1.1%
96.95140099 1
1.1%
93.26919001 1
1.1%
92.53391006 1
1.1%
91.77713339 1
1.1%
86.99079799 1
1.1%
2025-05-02T17:16:41.002176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

rsi_60
Numeric time series

High correlation  Missing  Non stationary 

Distinct16
Distinct (%)100.0%
Missing72
Missing (%)81.8%
Infinite0
Infinite (%)0.0%
Mean68.532002
Minimum55.05644
Maximum96.719481
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:41.305734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum55.05644
5-th percentile55.666511
Q162.865124
median65.384414
Q370.104557
95-th percentile88.063445
Maximum96.719481
Range41.663041
Interquartile range (IQR)7.2394335

Descriptive statistics

Standard deviation11.315019
Coefficient of variation (CV)0.16510563
Kurtosis1.3913795
Mean68.532002
Median Absolute Deviation (MAD)3.418386
Skewness1.2998277
Sum1096.512
Variance128.02966
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9968870258
2025-05-02T17:16:41.426795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
2025-05-02T17:16:41.642233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:41.672645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
62.94673406 1
 
1.1%
84.05311021 1
 
1.1%
85.1781 1
 
1.1%
73.67496609 1
 
1.1%
65.7832569 1
 
1.1%
68.69117923 1
 
1.1%
62.62029377 1
 
1.1%
63.69131024 1
 
1.1%
55.05643989 1
 
1.1%
64.98557172 1
 
1.1%
Other values (6) 6
 
6.8%
(Missing) 72
81.8%
ValueCountFrequency (%)
55.05643989 1
1.1%
55.86986767 1
1.1%
58.52366872 1
1.1%
62.62029377 1
1.1%
62.94673406 1
1.1%
63.64888175 1
1.1%
63.69131024 1
1.1%
64.98557172 1
1.1%
65.7832569 1
1.1%
66.15475308 1
1.1%
ValueCountFrequency (%)
96.71948064 1
1.1%
85.1781 1
1.1%
84.05311021 1
1.1%
73.67496609 1
1.1%
68.9144213 1
1.1%
68.69117923 1
1.1%
66.15475308 1
1.1%
65.7832569 1
1.1%
64.98557172 1
1.1%
63.69131024 1
1.1%
2025-05-02T17:16:41.489628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

macd_7
Numeric time series

High correlation  Missing 

Distinct55
Distinct (%)100.0%
Missing33
Missing (%)37.5%
Infinite0
Infinite (%)0.0%
Mean909.4111
Minimum-46.336877
Maximum3182.6921
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:41.245898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-46.336877
5-th percentile-38.081256
Q147.789147
median77.865454
Q32089.0607
95-th percentile3009.4401
Maximum3182.6921
Range3229.0289
Interquartile range (IQR)2041.2715

Descriptive statistics

Standard deviation1217.5068
Coefficient of variation (CV)1.338786
Kurtosis-0.99454448
Mean909.4111
Median Absolute Deviation (MAD)103.66318
Skewness0.89675077
Sum50017.61
Variance1482322.8
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.010743608 × 10-15
2025-05-02T17:16:41.315589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:41.505071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:41.543942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1406.41221 1
 
1.1%
66.47890266 1
 
1.1%
61.3593469 1
 
1.1%
46.2650271 1
 
1.1%
49.31326733 1
 
1.1%
58.3537162 1
 
1.1%
62.03983584 1
 
1.1%
69.99164323 1
 
1.1%
74.15686735 1
 
1.1%
75.6401906 1
 
1.1%
Other values (45) 45
51.1%
(Missing) 33
37.5%
ValueCountFrequency (%)
-46.33687702 1
1.1%
-43.91673306 1
1.1%
-42.58238271 1
1.1%
-36.15220162 1
1.1%
-25.79772196 1
1.1%
-13.15072906 1
1.1%
-6.108345922 1
1.1%
5.027782395 1
1.1%
16.05441625 1
1.1%
29.76180956 1
1.1%
ValueCountFrequency (%)
3182.692053 1
1.1%
3079.258906 1
1.1%
3012.222085 1
1.1%
3008.247752 1
1.1%
2993.545605 1
1.1%
2989.337435 1
1.1%
2975.372413 1
1.1%
2965.406897 1
1.1%
2860.601209 1
1.1%
2796.839693 1
1.1%
2025-05-02T17:16:41.363646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

macdsignal_7
Numeric time series

High correlation  Missing 

Distinct55
Distinct (%)100.0%
Missing33
Missing (%)37.5%
Infinite0
Infinite (%)0.0%
Mean889.7116
Minimum-408.02254
Maximum4394.8916
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:41.611699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-408.02254
5-th percentile-18.215847
Q144.940804
median83.033287
Q31247.7039
95-th percentile4059.9214
Maximum4394.8916
Range4802.9141
Interquartile range (IQR)1202.7631

Descriptive statistics

Standard deviation1395.1793
Coefficient of variation (CV)1.5681253
Kurtosis0.77801025
Mean889.7116
Median Absolute Deviation (MAD)71.197474
Skewness1.47901
Sum48934.138
Variance1946525.2
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.838131916 × 10-19
2025-05-02T17:16:41.674869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:41.861818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:41.901687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
-408.0225449 1
 
1.1%
69.74967067 1
 
1.1%
55.99064832 1
 
1.1%
40.2199871 1
 
1.1%
28.63297472 1
 
1.1%
17.13836521 1
 
1.1%
15.76100137 1
 
1.1%
25.06476279 1
 
1.1%
43.33751298 1
 
1.1%
58.85768064 1
 
1.1%
Other values (45) 45
51.1%
(Missing) 33
37.5%
ValueCountFrequency (%)
-408.0225449 1
1.1%
-40.78212664 1
1.1%
-30.3421415 1
1.1%
-13.0188631 1
1.1%
-0.4339979768 1
1.1%
11.83581374 1
1.1%
15.76100137 1
1.1%
17.13836521 1
1.1%
24.77266553 1
1.1%
25.06476279 1
1.1%
ValueCountFrequency (%)
4394.891563 1
1.1%
4305.273031 1
1.1%
4100.067791 1
1.1%
4042.715857 1
1.1%
3912.237403 1
1.1%
3613.967986 1
1.1%
3367.960236 1
1.1%
2956.171039 1
1.1%
2602.828332 1
1.1%
2441.116478 1
1.1%
2025-05-02T17:16:41.731053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

macdhist_7
Numeric time series

Missing 

Distinct55
Distinct (%)100.0%
Missing33
Missing (%)37.5%
Infinite0
Infinite (%)0.0%
Mean19.699498
Minimum-1732.9046
Maximum2194.8202
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:42.013511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1732.9046
5-th percentile-1514.1757
Q1-133.8926
median-29.56352
Q318.731401
95-th percentile2055.2364
Maximum2194.8202
Range3927.7248
Interquartile range (IQR)152.624

Descriptive statistics

Standard deviation918.59913
Coefficient of variation (CV)46.630585
Kurtosis1.0638643
Mean19.699498
Median Absolute Deviation (MAD)60.382874
Skewness0.73570096
Sum1083.4724
Variance843824.36
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.870037605 × 10-6
2025-05-02T17:16:42.112935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:42.369380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:42.412192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1814.434755 1
 
1.1%
-3.270768013 1
 
1.1%
5.368698576 1
 
1.1%
6.045039999 1
 
1.1%
20.68029262 1
 
1.1%
41.21535098 1
 
1.1%
46.27883447 1
 
1.1%
44.92688044 1
 
1.1%
30.81935437 1
 
1.1%
16.78250997 1
 
1.1%
Other values (45) 45
51.1%
(Missing) 33
37.5%
ValueCountFrequency (%)
-1732.904615 1
1.1%
-1707.597357 1
1.1%
-1527.574477 1
1.1%
-1508.433338 1
1.1%
-1266.219013 1
1.1%
-1182.114648 1
1.1%
-1080.466535 1
1.1%
-918.6917975 1
1.1%
-840.0890352 1
1.1%
-616.3281429 1
1.1%
ValueCountFrequency (%)
2194.8202 1
1.1%
2087.837398 1
1.1%
2078.692414 1
1.1%
2045.183772 1
1.1%
2016.390633 1
1.1%
1814.434755 1
1.1%
1585.612081 1
1.1%
928.6310151 1
1.1%
372.5440811 1
1.1%
46.27883447 1
1.1%
2025-05-02T17:16:42.175030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

obv
Numeric time series

High correlation  Non stationary  Unique 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1184.3325
Minimum0.00909259
Maximum6942.5572
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2025-05-02T17:16:42.491334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.00909259
5-th percentile0.066201653
Q11.1428574
median12.150111
Q32935.0286
95-th percentile5274.1399
Maximum6942.5572
Range6942.5481
Interquartile range (IQR)2933.8857

Descriptive statistics

Standard deviation1988.1625
Coefficient of variation (CV)1.6787198
Kurtosis0.28625524
Mean1184.3325
Median Absolute Deviation (MAD)12.118382
Skewness1.3438858
Sum104221.26
Variance3952790.2
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3953768888
2025-05-02T17:16:42.598025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2025-05-02T17:16:42.804326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-02T17:16:42.852619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.02358871 1
 
1.1%
0.06549571 1
 
1.1%
35.72536297 1
 
1.1%
0.44410517 1
 
1.1%
0.5452669 1
 
1.1%
1.4390618 1
 
1.1%
1.37639748 1
 
1.1%
0.47967044 1
 
1.1%
0.0398695 1
 
1.1%
0.99766936 1
 
1.1%
Other values (78) 78
88.6%
ValueCountFrequency (%)
0.00909259 1
1.1%
0.01447002 1
1.1%
0.02358871 1
1.1%
0.0398695 1
1.1%
0.06549571 1
1.1%
0.06751269 1
1.1%
0.07131972 1
1.1%
0.1566265 1
1.1%
0.15855975 1
1.1%
0.1670844 1
1.1%
ValueCountFrequency (%)
6942.557214 1
1.1%
6169.892114 1
1.1%
5813.447481 1
1.1%
5650.73679 1
1.1%
5503.569224 1
1.1%
4848.056979 1
1.1%
4834.93225 1
1.1%
4690.111921 1
1.1%
4649.273713 1
1.1%
4223.466789 1
1.1%
2025-05-02T17:16:42.650040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Interactions

2025-05-02T17:16:31.509358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:08.896722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:09.965743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.163462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.645688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.584850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:13.963344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.990442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.067974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.638747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.011157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.064066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.088315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.914150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.892270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:22.993527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.341545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.406982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.450307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.352761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.278619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.354663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.436752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.562805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:08.940346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:10.007477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.198075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.680495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.634243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.002144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.028518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.113342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.683437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.057075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.106703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.126935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.955589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.936611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:23.038217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.381685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.446070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.497507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.390098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.321068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.394253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.482274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.611027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:08.993512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:10.055022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.239120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.720917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.706003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.043669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.073546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.159029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.970926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.098216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.147284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.172001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.005787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.977649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:23.081164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.426504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.494343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.542793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.426032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.365292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.441201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.533933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.659260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:09.036006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:10.097512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.274621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.761228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.748965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.083255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.112483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.204671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:17.012151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.142896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.190691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.212770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.047338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:22.023681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:23.126943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.468485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.547513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.588500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.463796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.405764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.481030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.582174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.702632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:09.074622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:10.140859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.312280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.793734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.797958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.120887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.151751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.248060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:17.057665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.183383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.233322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.086427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.088347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:22.064981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:23.175262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.516490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.586350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.629265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.502255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.445992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.521875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.629240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.762237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:09.116401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:10.186394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-02T17:16:11.831851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.847282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-02T17:16:21.130355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-02T17:16:48.707469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-02T17:16:31.815652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-02T17:16:11.870645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.925936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.201704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.263864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.335050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:17.159584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.273750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-02T17:16:13.414400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.625484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.725110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.278041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:17.616240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.686509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.748034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.557170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.507753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:22.575301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:23.958317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.049515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.094769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.703377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.971142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.992676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.064881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.531790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:32.277983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:09.664667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:10.864964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.369355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.303124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:13.456825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.669493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.763806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.330434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:17.661386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.731127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.788984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.600776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.576461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:22.621779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.003473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.096936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.137264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.033656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.012581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.036232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.121032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.577988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:32.318787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:09.699362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:10.905005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.406030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.340226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:13.709536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.722060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.803869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.377564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:17.719931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.770894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.832028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.641947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.619555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:22.666214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.055197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.142813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.188855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.074450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.053507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.087270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.175564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.621655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:32.376144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:09.740523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:10.949422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.445176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.380461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:13.751780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.765064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.846467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.422676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:17.780864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.815284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.873514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.686127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.663947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:22.713027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.099120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.191902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.236427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.118558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.090903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.135308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.217782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:53.691067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:32.422136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:09.778339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:10.984679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.481978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.417418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:13.785204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.803872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.882437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.459432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:17.818138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.851315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.911386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.727731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.699374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:22.761870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.143486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.232092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.278448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.156156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.126380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.174196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.261038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.221035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:32.485322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:09.839416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.029119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.520978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.455768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:13.828215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.850222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.932515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.501191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:17.870526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.896160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.956723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.783524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.747603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:22.810097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.195754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.274027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.323111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.202922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.165044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.217700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.306407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.291529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:32.596668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:09.880202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.069574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.558353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.493259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:13.869933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.893776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:15.972304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.546753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:17.911959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:18.957121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.000450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.825068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.789379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:22.879721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.246622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.320897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.363633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.243669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.206244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.262569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.345900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.353410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:32.666825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:09.923744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.117584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:11.600454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:12.536969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:13.917093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:14.945775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.020487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:16.592259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:17.963267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:19.016640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.045048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:20.869457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:21.847091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:22.944074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:24.295917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:25.362603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:26.407619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:27.299947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:28.243159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:29.307910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:30.390955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-02T17:16:31.432594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-02T17:16:43.003662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
closeema_14ema_21ema_60ema_7highlowmacd_7macdhist_7macdsignal_7obvopenrsi_14rsi_21rsi_60rsi_7sma_14sma_21sma_60sma_7volumewindow_end_mswindow_start_ms
close1.0000.9040.825-0.9090.9580.9910.992-0.633-0.002-0.485-0.5110.9820.0470.035-0.7010.0420.9040.825-0.9090.958-0.5110.8420.842
ema_140.9041.0000.979-0.1130.9680.9050.923-0.862-0.124-0.605-0.5570.922-0.206-0.152-0.294-0.4101.0000.979-0.1130.968-0.5570.9750.975
ema_210.8250.9791.0000.8210.9130.8230.857-0.902-0.114-0.672-0.4090.851-0.514-0.2480.474-0.6960.9791.0000.8210.913-0.4090.9950.995
ema_60-0.909-0.1130.8211.000-0.914-0.917-0.879-0.990-0.243-0.946-0.039-0.928-0.953-0.8730.800-0.767-0.1130.8211.000-0.914-0.0391.0001.000
ema_70.9580.9680.913-0.9141.0000.9610.965-0.723-0.085-0.515-0.5470.971-0.080-0.021-0.779-0.1580.9680.913-0.9141.000-0.5470.8870.887
high0.9910.9050.823-0.9170.9611.0000.985-0.6320.001-0.482-0.4860.9870.0250.046-0.8030.0210.9050.823-0.9170.961-0.4860.8380.838
low0.9920.9230.857-0.8790.9650.9851.000-0.673-0.012-0.519-0.5360.9900.025-0.022-0.686-0.0060.9230.857-0.8790.965-0.5360.8430.843
macd_7-0.633-0.862-0.902-0.990-0.723-0.632-0.6731.0000.0370.7970.332-0.6500.7300.304-0.7880.717-0.862-0.902-0.990-0.7230.332-0.907-0.907
macdhist_7-0.002-0.124-0.114-0.243-0.0850.001-0.0120.0371.000-0.4290.094-0.027-0.189-0.4960.1090.275-0.124-0.114-0.243-0.0850.094-0.097-0.097
macdsignal_7-0.485-0.605-0.672-0.946-0.515-0.482-0.5190.797-0.4291.0000.118-0.4880.7240.589-0.7790.490-0.605-0.672-0.946-0.5150.118-0.680-0.680
obv-0.511-0.557-0.409-0.039-0.547-0.486-0.5360.3320.0940.1181.000-0.514-0.0240.0840.0530.043-0.557-0.409-0.039-0.5471.000-0.223-0.223
open0.9820.9220.851-0.9280.9710.9870.990-0.650-0.027-0.488-0.5141.0000.0150.011-0.827-0.0240.9220.851-0.9280.971-0.5140.8380.838
rsi_140.047-0.206-0.514-0.953-0.0800.0250.0250.730-0.1890.724-0.0240.0151.0000.399-0.7620.583-0.206-0.514-0.953-0.080-0.024-0.268-0.268
rsi_210.035-0.152-0.248-0.873-0.0210.046-0.0220.304-0.4960.5890.0840.0110.3991.000-0.6760.253-0.152-0.248-0.873-0.0210.084-0.256-0.256
rsi_60-0.701-0.2940.4740.800-0.779-0.803-0.686-0.7880.109-0.7790.053-0.827-0.762-0.6761.000-0.426-0.2940.4740.800-0.7790.0530.8000.800
rsi_70.042-0.410-0.696-0.767-0.1580.021-0.0060.7170.2750.4900.043-0.0240.5830.253-0.4261.000-0.410-0.696-0.767-0.1580.043-0.181-0.181
sma_140.9041.0000.979-0.1130.9680.9050.923-0.862-0.124-0.605-0.5570.922-0.206-0.152-0.294-0.4101.0000.979-0.1130.968-0.5570.9750.975
sma_210.8250.9791.0000.8210.9130.8230.857-0.902-0.114-0.672-0.4090.851-0.514-0.2480.474-0.6960.9791.0000.8210.913-0.4090.9950.995
sma_60-0.909-0.1130.8211.000-0.914-0.917-0.879-0.990-0.243-0.946-0.039-0.928-0.953-0.8730.800-0.767-0.1130.8211.000-0.914-0.0391.0001.000
sma_70.9580.9680.913-0.9141.0000.9610.965-0.723-0.085-0.515-0.5470.971-0.080-0.021-0.779-0.1580.9680.913-0.9141.000-0.5470.8870.887
volume-0.511-0.557-0.409-0.039-0.547-0.486-0.5360.3320.0940.1181.000-0.514-0.0240.0840.0530.043-0.557-0.409-0.039-0.5471.000-0.223-0.223
window_end_ms0.8420.9750.9951.0000.8870.8380.843-0.907-0.097-0.680-0.2230.838-0.268-0.2560.800-0.1810.9750.9951.0000.887-0.2231.0001.000
window_start_ms0.8420.9750.9951.0000.8870.8380.843-0.907-0.097-0.680-0.2230.838-0.268-0.2560.800-0.1810.9750.9951.0000.887-0.2231.0001.000

Missing values

2025-05-02T17:16:32.772764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-02T17:16:32.908115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-02T17:16:33.106033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

pairopenhighlowclosevolumewindow_start_mswindow_end_mscandle_secondssma_7sma_14sma_21sma_60ema_7ema_14ema_21ema_60rsi_7rsi_14rsi_21rsi_60macd_7macdsignal_7macdhist_7obv
1746193740000BTC/USD96867.596872.296867.496872.20.0235891746193740000174619380000060NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.023589
1746193800000BTC/USD96872.296934.496872.296885.20.0654961746193800000174619386000060NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.065496
1746193860000BTC/USD96885.296885.296877.096877.00.0675131746193860000174619392000060NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.067513
1746193920000BTC/USD96877.096877.096859.096859.01.9143461746193920000174619398000060NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.914346
1746193980000BTC/USD96859.196876.596859.196876.50.0144701746193980000174619404000060NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.014470
1746194040000BTC/USD96876.496892.096876.096892.00.4333871746194040000174619410000060NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.433387
1746194100000BTC/USD96892.096892.096892.096892.00.009093174619410000017461941600006096879.128571NaNNaNNaN96879.128571NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.009093
1746194160000BTC/USD96891.996918.496891.996918.30.156627174619416000017461942200006096885.714286NaNNaNNaN96885.714286NaNNaNNaN73.401015NaNNaNNaNNaNNaNNaN0.156627
1746194220000BTC/USD96921.096921.096795.496795.41.887600174619422000017461942800006096872.885714NaNNaNNaN96872.885714NaNNaNNaN28.454894NaNNaNNaNNaNNaNNaN1.887600
1746194280000BTC/USD96795.596811.496782.696796.031.236413174619428000017461943400006096861.314286NaNNaNNaN96861.314286NaNNaNNaN29.830677NaNNaNNaNNaNNaNNaN31.236413
pairopenhighlowclosevolumewindow_start_mswindow_end_mscandle_secondssma_7sma_14sma_21sma_60ema_7ema_14ema_21ema_60rsi_7rsi_14rsi_21rsi_60macd_7macdsignal_7macdhist_7obv
1746198420000BTC/USD97804.097804.197804.097804.17.307047174619842000017461984800006097830.92857197830.66428697758.00476294152.35166797830.92857197830.66428697758.00476294152.35166730.16574669.41039169.04222264.98557229.76181066.196104-36.4342947.307047
1746198480000BTC/USD97804.197804.197716.197717.511.095964174619848000017461985400006097808.18571497829.26428697766.29523894349.72833397808.18571497829.26428697766.29523894349.72833320.16491847.90598361.09906963.69131016.05441664.277907-48.22349111.095964
1746198540000BTC/USD97717.597717.597717.497717.41.414107174619854000017461986000006097784.34285797821.26428697774.58095294532.62166797784.34285797821.26428697774.58095294532.62166717.81720035.09845761.09128062.9467345.02778254.315847-49.2880651.414107
1746198600000BTC/USD97717.497717.497678.997701.11.396909174619860000017461986600006097758.08571497812.10000097779.40000094709.53000097758.08571497812.10000097779.40000094709.53000016.58181833.63937856.79924862.620294-6.10834638.404521-44.5128671.396909
1746198660000BTC/USD97701.197701.297701.197701.20.724381174619866000017461987200006097749.90000097800.22142997781.08571494948.38333397749.90000097800.22142997781.08571494948.38333330.73302026.51793352.60908068.691179-13.15072924.772666-37.9233950.724381
1746198720000BTC/USD97701.297701.297638.897638.83.367922174619872000017461987800006097726.30000097786.08571497779.80000095133.89500097726.30000097786.08571497779.80000095133.8950000.12077324.34534648.17764665.783257-25.79772211.835814-37.6335363.367922
1746198780000BTC/USD97638.897656.997617.797617.75.578315174619878000017461988400006097699.68571497770.43571497777.50476295382.35666797699.68571497770.43571497777.50476295382.3566670.10712423.06368346.83644073.674966-36.152202-0.433998-35.7182045.578315
1746198840000BTC/USD97642.097642.097617.797619.81.332955174619884000017461989000006097673.35714397752.14285797778.22857195701.71166797673.35714397752.14285797778.22857195701.7116671.16587215.36380851.08169785.178100-42.582383-13.018863-29.5635201.332955
1746198900000BTC/USD97619.897640.097617.897638.91.725499174619890000017461989600006097662.12857197735.15714397773.55238195989.04500097662.12857197735.15714397773.55238195989.04500017.57425719.35567041.66666784.053110-46.336877-30.342141-15.9947361.725499
1746198960000BTC/USD97638.997666.797638.997666.60.306867174619896000017461990200006097654.87142997719.60714397765.80000096332.07500097654.87142997719.60714397765.80000096332.07500032.93010823.32761634.48341696.719481-43.916733-40.782127-3.1346060.306867